A widely used technique for matching images uses feature points that are obtained from the images, and examples of the technique include an image retrieval that uses an image as a query, and creation of a panoramic image that is performed by image composition. For example, there is a method for determining a coordinate transformation coefficient for performing a coordinate transformation process so that the coordinates of a feature point of one image from among two images agree with the coordinates of a feature point of the other image. A method is also known for determining that spoofing has been attempted in a case in which an error between conversion coordinates that are obtained by executing a planar projective transformation of an image from a first image to a second image and feature point coordinates in the second image is not more than a fixed value with respect to the first and second images that are obtained by photographing an object that is an inspection target from angles different from each other. A linear transformation matrix calculation device and an image retrieval that is used for fingerprint matching are also known. The linear transformation matrix calculation device can obtain a linear transformation matrix that reduces the similarity between partial spaces that belong to the same category while increasing the similarity between partial spaces that belong to different categories. A method for recognizing a plurality of advertisement signs in a video is also known, and the method can separate a corresponding point of an individual recognition target from a correspondence result that includes an error correspondence and mixed corresponding points obtained by using a local constant feature amount, and performs recognition on the basis of the corresponding point. At that time, deformation that is unlikely to occur as a result of changes in a viewpoint position and an attitude of a recognition target is eliminated (For example, see patent documents 1-5).
An example of a projective transformation matrix estimation method between two images, which is used for image matching, is Random Sample Consensus (RANSAC) (For example, see non-patent document 1). In addition, a method for limiting a selection range of m feature point pairs by using clustering is known (For example, see non-patent document 2). A method for performing reliable matching in images that are obtained by photographing different scenes etc., is also known (for example, see non-patent document 3).
[Patent Document 1] Japanese Laid-open Patent Publication No. 2010-272091
[Patent Document 2] International Publication Pamphlet No. WO 2010/050206
[Patent Document 3] Japanese National Publication of International Patent Application No. 2008-529156
[Patent Document 4] Japanese Laid-open Patent Publication No. 2010-182013
[Patent Document 5] Japanese Laid-open Patent Publication No. 2007-140613
[Non-Patent Document 1] Fischler, M. A. and Bolles, R. C. “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, ” Communications of the ACM, 24(6) :381-395, 1981.
[Non-Patent Document 2] Kai Ni, Hailin Jin, and Frank Dellaert, GroupSAC: Efficient Consensus in the Presence of Groupings, IEEE International Conference on Computer Vision (ICCV), 2009.
[Non-Patent Document 3] Lowe, G. David, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.